pm4py.algo.discovery.heuristics.variants package#
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.
Website: https://processintelligence.solutions Contact: info@processintelligence.solutions
Submodules#
pm4py.algo.discovery.heuristics.variants.classic module#
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.
Website: https://processintelligence.solutions Contact: info@processintelligence.solutions
- class pm4py.algo.discovery.heuristics.variants.classic.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
Bases:
Enum- ACTIVITY_KEY = 'pm4py:param:activity_key'#
- START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'#
- TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
- CASE_ID_KEY = 'pm4py:param:case_id_key'#
- DEPENDENCY_THRESH = 'dependency_thresh'#
- AND_MEASURE_THRESH = 'and_measure_thresh'#
- MIN_ACT_COUNT = 'min_act_count'#
- MIN_DFG_OCCURRENCES = 'min_dfg_occurrences'#
- DFG_PRE_CLEANING_NOISE_THRESH = 'dfg_pre_cleaning_noise_thresh'#
- LOOP_LENGTH_TWO_THRESH = 'loop_length_two_thresh'#
- HEU_NET_DECORATION = 'heu_net_decoration'#
- pm4py.algo.discovery.heuristics.variants.classic.apply(log: EventLog, parameters: Dict[str | Parameters, Any] | None = None) Tuple[PetriNet, Marking, Marking][source]#
Discovers a Petri net using Heuristics Miner
Parameters#
- log
Event log
- parameters
Possible parameters of the algorithm, including:
Parameters.ACTIVITY_KEY
Parameters.TIMESTAMP_KEY
Parameters.CASE_ID_KEY
Parameters.DEPENDENCY_THRESH
Parameters.AND_MEASURE_THRESH
Parameters.MIN_ACT_COUNT
Parameters.MIN_DFG_OCCURRENCES
Parameters.DFG_PRE_CLEANING_NOISE_THRESH
Parameters.LOOP_LENGTH_TWO_THRESH
Returns#
- net
Petri net
- im
Initial marking
- fm
Final marking
- pm4py.algo.discovery.heuristics.variants.classic.apply_pandas(df: DataFrame, parameters: Dict[str | Parameters, Any] | None = None) Tuple[PetriNet, Marking, Marking][source]#
Discovers a Petri net using Heuristics Miner
Parameters#
- df
Pandas dataframe
- parameters
Possible parameters of the algorithm, including: activity_key, case_id_glue, timestamp_key, dependency_thresh, and_measure_thresh, min_act_count, min_dfg_occurrences, dfg_pre_cleaning_noise_thresh, loops_length_two_thresh
Returns#
- net
Petri net
- im
Initial marking
- fm
Final marking
- pm4py.algo.discovery.heuristics.variants.classic.apply_dfg(dfg: Dict[Tuple[str, str], int], activities=None, activities_occurrences=None, start_activities=None, end_activities=None, parameters: Dict[Any, Any] | None = None) Tuple[PetriNet, Marking, Marking][source]#
Discovers a Petri net using Heuristics Miner
Parameters#
- dfg
Directly-Follows Graph
- activities
(If provided) list of activities of the log
- activities_occurrences
(If provided) dictionary of activities occurrences
- start_activities
(If provided) dictionary of start activities occurrences
- end_activities
(If provided) dictionary of end activities occurrences
- parameters
Possible parameters of the algorithm, including:
Parameters.ACTIVITY_KEY
Parameters.TIMESTAMP_KEY
Parameters.CASE_ID_KEY
Parameters.DEPENDENCY_THRESH
Parameters.AND_MEASURE_THRESH
Parameters.MIN_ACT_COUNT
Parameters.MIN_DFG_OCCURRENCES
Parameters.DFG_PRE_CLEANING_NOISE_THRESH
Parameters.LOOP_LENGTH_TWO_THRESH
Returns#
- net
Petri net
- im
Initial marking
- fm
Final marking
- pm4py.algo.discovery.heuristics.variants.classic.apply_heu(log: EventLog, parameters: Dict[Any, Any] | None = None) HeuristicsNet[source]#
Discovers an Heuristics Net using Heuristics Miner
Parameters#
- log
Event log
- parameters
Possible parameters of the algorithm, including:
Parameters.ACTIVITY_KEY
Parameters.TIMESTAMP_KEY
Parameters.CASE_ID_KEY
Parameters.DEPENDENCY_THRESH
Parameters.AND_MEASURE_THRESH
Parameters.MIN_ACT_COUNT
Parameters.MIN_DFG_OCCURRENCES
Parameters.DFG_PRE_CLEANING_NOISE_THRESH
Parameters.LOOP_LENGTH_TWO_THRESH
Returns#
- heu
Heuristics Net
- pm4py.algo.discovery.heuristics.variants.classic.apply_heu_pandas(df: DataFrame, parameters: Dict[str | Parameters, Any] | None = None) HeuristicsNet[source]#
Discovers an Heuristics Net using Heuristics Miner
Parameters#
- df
Pandas dataframe
- parameters
Possible parameters of the algorithm, including:
Parameters.ACTIVITY_KEY
Parameters.TIMESTAMP_KEY
Parameters.CASE_ID_KEY
Parameters.DEPENDENCY_THRESH
Parameters.AND_MEASURE_THRESH
Parameters.MIN_ACT_COUNT
Parameters.MIN_DFG_OCCURRENCES
Parameters.DFG_PRE_CLEANING_NOISE_THRESH
Parameters.LOOP_LENGTH_TWO_THRESH
Returns#
- heu
Heuristics Net
- pm4py.algo.discovery.heuristics.variants.classic.apply_heu_dfg(dfg, activities=None, activities_occurrences=None, start_activities=None, end_activities=None, dfg_window_2=None, freq_triples=None, performance_dfg=None, parameters=None) HeuristicsNet[source]#
Discovers an Heuristics Net using Heuristics Miner
Parameters#
- dfg
Directly-Follows Graph
- activities
(If provided) list of activities of the log
- activities_occurrences
(If provided) dictionary of activities occurrences
- start_activities
(If provided) dictionary of start activities occurrences
- end_activities
(If provided) dictionary of end activities occurrences
- dfg_window_2
(If provided) DFG of window 2
- freq_triples
(If provided) Frequency triples
- performance_dfg
(If provided) Performance DFG
- parameters
Possible parameters of the algorithm, including:
Parameters.ACTIVITY_KEY
Parameters.TIMESTAMP_KEY
Parameters.CASE_ID_KEY
Parameters.DEPENDENCY_THRESH
Parameters.AND_MEASURE_THRESH
Parameters.MIN_ACT_COUNT
Parameters.MIN_DFG_OCCURRENCES
Parameters.DFG_PRE_CLEANING_NOISE_THRESH
Parameters.LOOP_LENGTH_TWO_THRESH
Returns#
- heu
Heuristics Net
- pm4py.algo.discovery.heuristics.variants.classic.calculate(heu_net, dependency_thresh=0.5, and_measure_thresh=0.65, min_act_count=1, min_dfg_occurrences=1, dfg_pre_cleaning_noise_thresh=0.05, loops_length_two_thresh=0.5, parameters=None)[source]#
Calculate the dependency matrix, populate the nodes
Parameters#
- dependency_thresh
(Optional) dependency threshold
- and_measure_thresh
(Optional) AND measure threshold
- min_act_count
(Optional) minimum number of occurrences of an activity
- min_dfg_occurrences
(Optional) minimum dfg occurrences
- dfg_pre_cleaning_noise_thresh
(Optional) DFG pre cleaning noise threshold
- loops_length_two_thresh
(Optional) loops length two threshold
- parameters
Other parameters of the algorithm
pm4py.algo.discovery.heuristics.variants.plusplus module#
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.
Website: https://processintelligence.solutions Contact: info@processintelligence.solutions
- class pm4py.algo.discovery.heuristics.variants.plusplus.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
Bases:
Enum- ACTIVITY_KEY = 'pm4py:param:activity_key'#
- START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'#
- TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
- CASE_ID_KEY = 'pm4py:param:case_id_key'#
- DEPENDENCY_THRESH = 'dependency_thresh'#
- AND_MEASURE_THRESH = 'and_measure_thresh'#
- MIN_ACT_COUNT = 'min_act_count'#
- MIN_DFG_OCCURRENCES = 'min_dfg_occurrences'#
- HEU_NET_DECORATION = 'heu_net_decoration'#
- pm4py.algo.discovery.heuristics.variants.plusplus.apply(log: EventLog, parameters: Dict[Any, Any] | None = None) Tuple[PetriNet, Marking, Marking][source]#
Discovers a Petri net using the Heuristics Miner ++ algorithm
Implements the approach described in Burattin, Andrea, and Alessandro Sperduti. “Heuristics Miner for Time Intervals.” ESANN. 2010.
https://andrea.burattin.net/public-files/publications/2010-esann-slides.pdf
Parameters#
- log
Event log
- parameters
Parameters of the algorithm, including: - Parameters.ACTIVITY_KEY - Parameters.START_TIMESTAMP_KEY - Parameters.TIMESTAMP_KEY - Parameters.DEPENDENCY_THRESH - Parameters.AND_MEASURE_THRESH - Parameters.MIN_ACT_COUNT - Parameters.MIN_DFG_OCCURRENCES - Parameters.HEU_NET_DECORATION
Returns#
- net
Petri net
- im
Initial marking
- fm
Final marking
- pm4py.algo.discovery.heuristics.variants.plusplus.apply_pandas(df: DataFrame, parameters: Dict[Any, Any] | None = None) Tuple[PetriNet, Marking, Marking][source]#
Discovers a Petri net using the Heuristics Miner ++ algorithm
Implements the approach described in Burattin, Andrea, and Alessandro Sperduti. “Heuristics Miner for Time Intervals.” ESANN. 2010.
https://andrea.burattin.net/public-files/publications/2010-esann-slides.pdf
Parameters#
- df
Dataframe
- parameters
Parameters of the algorithm, including: - Parameters.ACTIVITY_KEY - Parameters.START_TIMESTAMP_KEY - Parameters.TIMESTAMP_KEY - Parameters.CASE_ID_KEY - Parameters.DEPENDENCY_THRESH - Parameters.AND_MEASURE_THRESH - Parameters.MIN_ACT_COUNT - Parameters.MIN_DFG_OCCURRENCES - Parameters.HEU_NET_DECORATION
Returns#
- net
Petri net
- im
Initial marking
- fm
Final marking
- pm4py.algo.discovery.heuristics.variants.plusplus.apply_heu(log: EventLog, parameters: Dict[Any, Any] | None = None) HeuristicsNet[source]#
Discovers an heuristics net using the Heuristics Miner ++ algorithm
Implements the approach described in Burattin, Andrea, and Alessandro Sperduti. “Heuristics Miner for Time Intervals.” ESANN. 2010.
https://andrea.burattin.net/public-files/publications/2010-esann-slides.pdf
Parameters#
- log
Event log
- parameters
Parameters of the algorithm, including: - Parameters.ACTIVITY_KEY - Parameters.START_TIMESTAMP_KEY - Parameters.TIMESTAMP_KEY - Parameters.DEPENDENCY_THRESH - Parameters.AND_MEASURE_THRESH - Parameters.MIN_ACT_COUNT - Parameters.MIN_DFG_OCCURRENCES - Parameters.HEU_NET_DECORATION
Returns#
- heu_net
Heuristics net
- pm4py.algo.discovery.heuristics.variants.plusplus.discover_abstraction_log(log: EventLog, parameters: Dict[Any, Any] | None = None) Tuple[Any, Any, Any, Any, Any, Any, Any][source]#
Discovers an abstraction from a log that is useful for the Heuristics Miner ++ algorithm
Parameters#
- log
Event log
- parameters
Parameters of the algorithm, including: - Parameters.ACTIVITY_KEY - Parameters.START_TIMESTAMP_KEY - Parameters.TIMESTAMP_KEY - Parameters.CASE_ID_KEY
Returns#
- start_activities
Start activities
- end_activities
End activities
- activities_occurrences
Activities along with their number of occurrences
- dfg
Directly-follows graph
- performance_dfg
(Performance) Directly-follows graph
- sojourn_time
Sojourn time for each activity
- concurrent_activities
Concurrent activities
- pm4py.algo.discovery.heuristics.variants.plusplus.apply_heu_pandas(df: DataFrame, parameters: Dict[Any, Any] | None = None) HeuristicsNet[source]#
Discovers an heuristics net using the Heuristics Miner ++ algorithm
Implements the approach described in Burattin, Andrea, and Alessandro Sperduti. “Heuristics Miner for Time Intervals.” ESANN. 2010.
https://andrea.burattin.net/public-files/publications/2010-esann-slides.pdf
Parameters#
- df
Dataframe
- parameters
Parameters of the algorithm, including: - Parameters.ACTIVITY_KEY - Parameters.START_TIMESTAMP_KEY - Parameters.TIMESTAMP_KEY - Parameters.CASE_ID_KEY - Parameters.DEPENDENCY_THRESH - Parameters.AND_MEASURE_THRESH - Parameters.MIN_ACT_COUNT - Parameters.MIN_DFG_OCCURRENCES - Parameters.HEU_NET_DECORATION
Returns#
- heu_net
Heuristics net
- pm4py.algo.discovery.heuristics.variants.plusplus.discover_abstraction_dataframe(df: DataFrame, parameters: Dict[Any, Any] | None = None) Tuple[Any, Any, Any, Any, Any, Any, Any][source]#
Discovers an abstraction from a dataframe that is useful for the Heuristics Miner ++ algorithm
Parameters#
- df
Dataframe
- parameters
Parameters of the algorithm, including: - Parameters.ACTIVITY_KEY - Parameters.START_TIMESTAMP_KEY - Parameters.TIMESTAMP_KEY - Parameters.CASE_ID_KEY
Returns#
- start_activities
Start activities
- end_activities
End activities
- activities_occurrences
Activities along with their number of occurrences
- dfg
Directly-follows graph
- performance_dfg
(Performance) Directly-follows graph
- sojourn_time
Sojourn time for each activity
- concurrent_activities
Concurrent activities
- pm4py.algo.discovery.heuristics.variants.plusplus.discover_heu_net_plus_plus(start_activities, end_activities, activities_occurrences, dfg, performance_dfg, sojourn_time, concurrent_activities, parameters: Dict[Any, Any] | None = None)[source]#
Discovers an heuristics net using the Heuristics Miner ++ algorithm
Implements the approach described in Burattin, Andrea, and Alessandro Sperduti. “Heuristics Miner for Time Intervals.” ESANN. 2010.
https://andrea.burattin.net/public-files/publications/2010-esann-slides.pdf
Parameters#
- start_activities
Start activities
- end_activities
End activities
- activities_occurrences
Activities along with their number of occurrences
- dfg
Directly-follows graph
- performance_dfg
(Performance) Directly-follows graph
- sojourn_time
Sojourn time for each activity
- concurrent_activities
Concurrent activities
- parameters
Parameters of the algorithm, including: - Parameters.DEPENDENCY_THRESH - Parameters.AND_MEASURE_THRESH - Parameters.MIN_ACT_COUNT - Parameters.MIN_DFG_OCCURRENCES - Parameters.HEU_NET_DECORATION
Returns#
- heu_net
Heuristics net
- pm4py.algo.discovery.heuristics.variants.plusplus.calculate(heu_net: HeuristicsNet, dependency_thresh: float, and_measure_thresh: float, heu_net_decoration: str) HeuristicsNet[source]#
Calculates the dependency matrix and the AND measures using the Heuristics Miner ++ formulas
Parameters#
- heu_net
Heuristics net
- dependency_thresh
Dependency threshold
- and_measure_thresh
AND measure threshold
- heu_net_decoration
Decoration to use (frequency/performance)
Returns#
- heu_net
Heuristics net
- pm4py.algo.discovery.heuristics.variants.plusplus.calculate_dependency(heu_net: HeuristicsNet, dependency_thresh: float, heu_net_decoration: str) HeuristicsNet[source]#
Calculates the dependency matrix using the Heuristics Miner ++ formula
Parameters#
- heu_net
Heuristics net
- dependency_thresh
Dependency threshold
- heu_net_decoration
Decoration to include (frequency/performance)
Returns#
- heu_net
Heuristics net (enriched)
- pm4py.algo.discovery.heuristics.variants.plusplus.calculate_and_out_measure(heu_net: HeuristicsNet, and_measure_thresh: float) HeuristicsNet[source]#
Calculates the AND measure for outgoing edges using the Heuristics Miner ++ formula
Parameters#
- heu_net
Heuristics net
- and_measure_thresh
And measure threshold
Returns#
- heu_net
Heuristics net (enriched)
- pm4py.algo.discovery.heuristics.variants.plusplus.calculate_and_in_measure(heu_net: HeuristicsNet, and_measure_thresh: float) HeuristicsNet[source]#
Calculates the AND measure for incoming edges using the Heuristics Miner ++ formula
Parameters#
- heu_net
Heuristics net
- and_measure_thresh
And measure threshold
Returns#
- heu_net
Heuristics net (enriched)